Panel Of Biomarkers Research Articles

A pharmacokinetic-pharmacodynamic analysis of l-glutamine for the treatment of sickle cell disease: Implications for understanding the mechanism of action and evaluating response to therapy.

The mechanisms of action of l-glutamine for the treatment of sickle cell disease (SCD) are not well understood and there are no validated clinical biomarkers to assess response. We conducted a three-week, dose-ascending trial of glutamine and measured the pharmacokinetic (PK) exposure parameters, peak concentration (Cmax) and area under the curve (AUC). We used a panel of biomarkers to investigate the pharmacodynamics (PD) of glutamine and studied PK-PD relationships. There was no plasma accumulation of glutamine, glutamate, arginine or other amino acids over time, but modestly improved arginine bioavailability was observed. In standard analysis by dose levels over time, there were no measurable effects on blood counts, viscosity, ektacytometry or reactive oxygen species (ROS). In PK-PD analysis, however, higher glutamine exposure (Cmax or AUC) was associated with increased whole blood viscosity and cellular dehydration, yet also with higher haemoglobin concentration, increased haematocrit-to-viscosity ratio, decreased reticulocyte ROS, improved RBC deformability and decreased point of sickling. This novel PK-PD analysis identified biomarkers reflecting the positive and negative effects of glutamine, helping to elucidate its mechanisms of action in SCD. PK-optimized dosing to achieve glutamine exposure (AUC or Cmax) that is associated with salutary biological effects should be studied to support its therapeutic use.

Characterizing the Linkage of Systemic Hypoxia and Angiogenesis in High-Grade Glioma to Define the Changes in Tumor Microenvironment for Predicting Prognosis.

High-grade gliomas (HGG) comprising WHO grades 3 and 4 have a poor overall survival (OS) that has not improved in the past decade. Herein, markers representing four components of the tumor microenvironment (TME) were identified to define their linked expression in TME and predict the prognosis in HGG, namely, interleukin6 (IL6, inflammation), inducible nitric oxide synthase(iNOS), heat shock protein-70 (HSP70, hypoxia), vascular endothelial growth receptor (VEGF), and endothelin1 (ET1) (angiogenesis) and matrix metalloprotease-14 (MMP14) and intercellular adhesion molecule1 (ICAM1, extracellular matrix). To establish a non-invasive panel of biomarkers for precise prognostication in HGG. Eighty-six therapy-naive HGG patients with 45 controls were analyzed for the defined panel. Systemic expression of extracellular/secretory biomarkers was screened dot-immune assay (DIA), quantified by ELISA, and validated by immunocytochemistry (ICC). Expression of iNOS, HSP70, IL-6, VEGF, ET1, MMP14, and ICAM1 was found to be positively associated with grade. Quantification of circulating levels of the markers by ELISA and ICC presented a similar result. The biomarkers were observed to negatively correlate with OS (p < 0.0001). Cox-regression analysis yielded all biomarkers as good prognostic indicators and independent of confounders. On applying combination statistics, the biomarker panel achieved higher sensitivity than single markers to define survival. The intra-association of all seven biomarkers was significant, hinting of a cross-talk between the TME components and a hypoxia driven systemic inflammation upregulating the expression of other components. This is a first ever experimental study of a marker panel that can distinguish between histopathological grades and also delineate differential survival using liquid biopsy, suggesting that markers of hypoxia can be a cornerstone for personalized therapy. The panel of biomarkers of iNOS, HSP70, IL-6, VEGF, ET1, MMP14, and ICAM1 holds promise for prognostication in HGG.

Urinary microbiome dysbiosis is associated with an inflammatory environment and perturbed fatty acids metabolism in the pathogenesis of bladder cancer

BackgroundBladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients’ outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved.MethodsA total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared.ResultsComparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00.ConclusionsMicrobiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development.

P-029 Genome-wide microRNA expression profiling in human spermatozoa and its relation to sperm quality

Abstract Study question Does the microRNA (miRNA) expression profile in human spermatozoa change with their morphological appearance? Summary answer This study revealed miRNA expression associated with good-quality sperm classified based on their morphology. These miRNAs could represent efficient biomarkers to evaluate male fertility status. What is known already We previously published a scoring scale where the spermatozoa with a Score 6 (Good spermatozoon) display normal head shape with symmetrical nuclear no extrusion and/or no invagination of the nuclear membrane, without any vacuole and normal base. The spermatozoa with a Score 0, display a nuclear-shape disorder with an abnormal base and at least one large vacuole (Bad spermatozoon). Despite the great magnitude of studies investigating the miRNA role in spermatogenesis, limited work has been done to evaluate it as a potential biomarker for sperm quality in relation to their morphology. Study design, size, duration In a prospective study from March 2023 to May 2023, twenty semen samples were collected. The samples were categorized according to their scoring morphology into Score 6 and Score 0. Ten samples (five with a Score 6 and five with a Score 0) were used for the analysis by microarray and ten (five with a Score 6, and five with a Score 0) for the miRNA expression analysis by RT-qPCR. Participants/materials, setting, methods Semen samples were scored according to their high-magnification morphology and stored at -80 °C. We used miRNA microarray chip to analyze miRNA profile. Statistical significance was evaluated by Significance Analysis of Microarrays, screened by Fold change&amp;gt;2 and p-values ≤ 0.05. The correlation between miRNAs and their corresponding mRNA targets was analyzed using in silico prediction algorithms. The methylome was studied using the DNA sequencing method. TaqMan miRNA assays were used to confirm the miRNA results. Main results and the role of chance We perform a robust transcriptomic analysis to define unique miRNA signatures able to identify the spermatozoa that have high scores regardless of morphological appearance at high magnification. There were 86 significantly overexpressed in Score 6 and 21 miRNAs in Score 0. Score 6-spermatozoa had significantly higher levels of hsa-miR-132 (FCx41, p=1.68E-8), hsa-miR-1973 (FCx32, p=6.19E-8), hsa-miR-34 (FCx30, p=8.43E-8), hsa-miR-25 (FCx27, p=1.30E-6), hsa-miR-342 (FCx24, p=1.57E-5), hsa-miR-15 (FCx14, p=8.56E-6), hsa-miR-125 (FCx13, p=6E-4), hsa-miR-30 (FCx12, p = 3.75E-6), than Score 0. The miRNAs overexpressed in Score 6 are predicted to target 910 messenger RNA involved in biological functions of the spermatozoa and several signaling pathways, such as mTOR, p53, and MAPK signaling pathways, as well as cell Intrinsic pathway for apoptosis, cellular senescence, and oxidative stress. Whereas genes targeted by score 0-miRNAs were implicated in the regulation of cellular growth and proliferation. Our data indicate a difference in methylation of the promoter region of miRNAs, and their validated target genes were observed between score 6 and score 0. Aberrant expression of the Score 6-miRs or their targets could be due to the methylation status of the promoter region and indicative of a defect in spermatogenesis. Limitations, reasons for caution This work provides a foundation for the interpretation of miRNA changes associated with spermatozoa morphological appearance. The data must be validated by TaqMan miRNA assays in a higher number of semen samples. Moreover, the molecular network of miRNA-targeted gene pathways also merits investigation. These experiments are currently ongoing. Wider implications of the findings The profiling of miRNAs repertoire in scored spermatozoa morphology opens new perspectives to explore male fertility status and provides a biomarker panel for sperm analysis during the ART procedure. Larger well-controlled investigations employing microRNAome approaches are needed to validate these conclusions. Trial registration number not applicable

Putative mRNA Biomarkers for the Eradication of Infection in an Equine Experimental Model of Septic Arthritis.

Septic arthritis (SA) in horses has long-term health implications. The success of its resolution hinges on the implementation of early, aggressive treatment, which is often sustained over a prolonged period. Common diagnostic methods do not allow for the reliable detection of the eradication of joint infection. A potential alternative is the discovery and characterization of mRNA biomarkers. The purpose of this study was to identify potential mRNA biomarkers for the eradication of joint infection in equine SA and to compare their expression with our previously published proteomics data. In addition, the transcriptomics data were compared to the mRNA biomarker panel, SeptiCyte Lab, used to distinguish sepsis from non-septic shock in humans. A comparative transcriptomics analysis of synovial fluid from the SA joints of five horses with active infection and subsequent post-treatment eradicated infection in the same joints and five horses with non-septic synovitis was performed. Eight novel mRNA transcripts were identified that were significantly upregulated (>3-fold) in horses with active SA compared to horses post-eradication of infection after treatment and horses with non-septic synovitis. Two proteins in our proteomics data corresponded to these mRNA transcripts, but were not statistically different. The transcripts used in the SeptiCyte test were not differentially expressed in our study. Our results suggest that mRNA may be a useful source of biomarkers for the eradication of joint infection in horses and warrants further investigation.

Beyond PSA: a multi-modal pilot study identifying novel proteomic biomarkers in paraffin-embedded prostate cancer tissue

Abstract Background Prostate cancer (PCa) is a common and complex disease in men, often progressing from localized to aggressive meta-static stages requiring advanced therapies. Early detection of PCa relies primarily on multiparametric tests, with limitations, like over-diagnosis and lack of specificity. Advances in molecular profiling, particularly proteomics, could enhance patient stratification and personalized therapies. Methods We conducted an analysis using Formalin-Fixed Paraffin-Embedded (FFPE) samples from 23 patients diagnosed with prostatic adenocarcinoma. Proteins were extracted from tissues, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The data were processed to identify proteins and subsequent bioinformatics analysis was performed to uncover significant pathways and potential biomarkers panel. Validation of identified biomarkers was carried out through Western blotting (WB). Results Proteomic profiling identified 1,159 proteins, including 176 significantly elevated in tumor tissues. Enrichment analysis highlighted their involvement in stress response, protein metabolism, and signaling pathways associated with PCa progression. Key pathways included mTORC1 signaling, Myc signaling, and focal adhesion. A biomarker panel consisting of KLK3, GDF15, MIF, and AZGP1 was proposed based on their discriminatory power in distinguishing tumor from normal tissues. WB confirmed the tumor-specific expression of these candidates, particularly GDF15 and KLK3. Conclusions The present study shows for the first time a multi-modal approach that combines a panel of PCa tissue proteins, as a new tool of PCa in patients. The identified biomarker panel offers promise for improving detection and tailoring personalized treatment strategies in PCa management. Further validation in larger cohorts and clinical settings is warranted to establish these findings.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SignificanceMass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs.We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation.Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument.Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data.Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

An Intestinal Microbiome Intervention Affects Biochemical Disease Activity in Patients with Antiphospholipid Syndrome.

Background The origin of autoantibodies in patients with antiphospholipid syndrome (APS) is unknown. The gut microbiome contributes to autoimmunity and contains peptide homologues to the main APS autoantigen, which affect disease activity in animal models. Alteration of the gut microbiota with vancomycin diminishes disease activity in mice but no data on the effect of gut microbiota alteration in APS patients are available to date. Objective To evaluate whether the gut microbiome affects disease activity in human APS. Methods This was a pre-post design intervention study in APS patients with stable disease and no gastrointestinal comorbidity. Subjects received oral vancomycin, 500 mg four times daily for 7 days, previously shown to alter gut microbiota composition without systemic effects. Disease activity was assessed at four time points by measuring a panel of clinical phenotype-related biomarkers: antiphospholipid antibodies (APLAs), complement and inflammation markers, and hemostatic parameters. The primary outcome was the composite of the biomarker panel determined by multilevel principal component analysis. Results A total of 15 subjects completed the study. The primary outcome, the first principal component of the biomarker panel data, was significantly different after 7 days of vancomycin treatment ( p = 0.03), but not at day 42. APLA titers were unaffected. Unexpectedly, 4 out of 15 patients were negative for APLAs at baseline. In a post-hoc analysis, there was a prolonged effect for subjects with positive antibodies at baseline ( p = 0.03). In subjects with negative APLAs at baseline, the intervention showed no effect. Conclusion The intestinal microbiome affects the biochemical disease activity in APS patients. The mechanism is yet unknown but appears to be APS-specific.

Identification of Novel DNA Methylation Prognostic Biomarkers for AML With Normal Cytogenetics.

AML is a hematologic cancer that is clinically heterogeneous, with a wide range of clinical outcomes. DNA methylation changes are a hallmark of AML but are not routinely used as a criterion for risk stratification. The aim of this study was to explore DNA methylation markers that could risk stratify patients with cytogenetically normal AML (CN-AML), currently classified as intermediate-risk. DNA methylation profiles in whole blood samples from 77 patients with CN-AML in The Cancer Genome Atlas (LAML cohort) were analyzed. Individual 5'-cytosine-phosphate-guanine-3' (CpG) sites were assessed for their ability to predict overall survival. The output was validated using DNA methylation profiles from bone marrow samples of 79 patients with CN-AML in a separate data set from the Gene Expression Omnibus. In the training set, using DNA methylation data derived from the 450K array, we identified 2,549 CpG sites that could potentially distinguish patients with CN-AML with an adverse prognosis (intermediate-poor) from those with a more favorable prognosis (intermediate-favorable) independent of age. Of these, 25 CpGs showed consistent prognostic potential across both the 450K and 27K array platforms. In a separate validation data set, nine of these 25 CpGs exhibited statistically significant differences in 2-year survival. These nine validated CpGs formed the basis for a combined prognostic biomarker panel, which includes an 8-CpG Somatic Panel and the methylation status of cg23947872. This panel displayed strong predictive ability for 2-year survival, 2-year progression-free survival, and complete remission in the validation cohort. This study highlights DNA methylation profiling as a promising approach to enhance risk stratification in patients with CN-AML, potentially offering a pathway to more personalized treatment strategies.

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Personalized drug screening using patient-derived organoid and its clinical relevance in gastric cancer

The efficacy of chemotherapy varies significantly among patients with gastric cancer (GC), and there is currently no effective strategy to predict chemotherapeutic outcomes. In this study, we successfully establish 57 GC patient-derived organoids (PDOs) from 73 patients with GC (78%). These organoids retain histological characteristics of their corresponding primary GC tissues. GC PDOs show varied responses to different chemotherapeutics. Through RNA sequencing, the upregulation of tumor suppression genes/pathways is identified in 5-fluorouracil (FU)- or oxaliplatin-sensitive organoids, whereas genes/pathways associated with proliferation and invasion are enriched in chemotherapy-resistant organoids. Gene expression biomarker panels, which could distinguish sensitive and resistant patients to 5-FU and oxaliplatin (area under the dose-response curve [AUC] >0.8), are identified. Moreover, the drug-response results in PDOs are validated in patient-derived organoids-based xenograft (PDOX) mice and are consistent with the actual clinical response in 91.7% (11/12) of patients with GC. Assessing chemosensitivity in PDOs can be utilized as a valuable tool for screening chemotherapeutic drugs in patients with GC.

Radiation Research Society Journal-based Historical Review of the Use of Biomarkers for Radiation Dose and Injury Assessment: Acute Health Effects Predictions.

A multiple-parameter based approach using radiation-induced clinical signs and symptoms, hematology changes, cytogenetic chromosomal aberrations, and molecular biomarkers changes after radiation exposure is used for biodosimetry-based dose assessment. In the current article, relevant milestones from Radiation Research are documented that forms the basis of the current consensus approach for diagnostics after radiation exposure. For example, in 1962 the use of cytogenetic chromosomal aberration using the lymphocyte metaphase spread dicentric assay for biodosimetry applications was first published in Radiation Research. This assay is now complimented using other cytogenetic chromosomal aberration assays (i.e., chromosomal translocations, cytokinesis-blocked micronuclei, premature chromosome condensation, γ-H2AX foci, etc.). Changes in blood cell counts represent an early-phase biomarker for radiation exposures. Molecular biomarker changes have evolved to include panels of organ-specific plasma proteomic and blood-based gene expression biomarkers for radiation dose assessment. Maturation of these assays are shown by efforts for automated processing and scoring, development of point-of-care diagnostics devices, service laboratories inter-comparison exercises, and applications for dose and injury assessments in radiation accidents. An alternative and complementary approach has been advocated with the focus to de-emphasize "dose" and instead focus on predicting acute or delayed health effects. The same biomarkers used for dose estimation (e.g., lymphocyte counts) can be used to directly predict the later developing severity degree of acute health effects without performing dose estimation as an additional or intermediate step. This review illustrates contributing steps toward these developments published in Radiation Research.

Transcriptomics-based liquid biopsy panel for early non-invasive identification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer

BackgroundThis study aimed to develop a novel six-gene expression biomarker panel to enhance the early detection and risk stratification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer (LAGC).MethodsWe used genome-wide transcriptome profiling and rigorous bioinformatics to identify a six-gene expression biomarker panel. This panel was validated across multiple clinical cohorts using both tissue and liquid biopsy samples to predict peritoneal recurrence and micrometastasis in patients with LAGC.ResultsThrough genome-wide expression profiling, we identified six mRNAs and developed a risk prediction model using 196 samples from a surgical specimen training cohort. This model, incorporating a 6-mRNA panel with clinical features, demonstrated high predictive accuracy for peritoneal recurrence in gastric cancer patients, with an AUC of 0.966 (95% CI: 0.944–0.988). Transitioning from invasive surgical or endoscopic biopsy to noninvasive liquid biopsy, the model retained its predictive efficacy (AUC = 0.963; 95% CI: 0.926–1.000). Additionally, the 6-mRNA panel effectively differentiated patients with or without peritoneal metastasis in 95 peripheral blood specimens (AUC = 0.970; 95% CI: 0.936–1.000) and identified peritoneal micrometastases with a high efficiency (AUC = 0.941; 95% CI: 0.874–1.000).ConclusionsOur study provides a novel gene expression biomarker panel that significantly enhances early detection of peritoneal recurrence and micrometastasis in patients with LAGC. The RSA model's predictive capability offers a promising tool for tailored treatment strategies, underscoring the importance of integrating molecular biomarkers with clinical parameters in precision oncology.

Cohort profile: Improved Pregnancy Outcomes via Early Detection (IMPROvED), an International Multicentre Prospective Cohort

Background Improved Pregnancy Outcomes via Early Detection (IMPROvED) is a multi-centre, European phase IIa clinical study. The primary aim of IMPROvED is to enable the assessment and refinement of innovative prototype preeclampsia risk assessment tests based on emerging biomarker technologies. Here we describe IMPROvED’s profile and invite researchers to collaborate. Methods A total of 4,038 low-risk nulliparous singleton pregnancies were recruited from maternity units in Ireland (N=1,501), United Kingdom (N=1,108), The Netherlands (N=810), and Sweden (N=619) between November 2013 to August 2017. Participants were interviewed by a research midwife at ~11 weeks (optional visit), ~15 weeks, ~20 weeks, ~34 weeks’ gestation (optional visit), and postpartum (within 72-hours following delivery). Findings to date Clinical data included information on maternal sociodemographic, medical history, and lifestyle factors collected at ~15 weeks’ gestation, and maternal measurements, collected at each study visit. Biobank samples included blood, urine, and hair collected at each study visit throughout pregnancy in all units plus umbilical cord/blood samples collected at birth in Ireland and Sweden. A total of 74.0% (N=2,922) had an uncomplicated pregnancy, 3.1% (N=122) developed preeclampsia, 3.6% (N=143) had a spontaneous preterm birth, and 10.5% (N=416) had a small for gestational age baby. We evaluated a panel of metabolite biomarkers and a panel of protein biomarkers at 15 weeks and 20 weeks’ gestation for preeclampsia risk assessment. Their translation into tests with clinical application, as conducted by commercial entities, was hampered by technical issues and changes in test requirements. Work on the panel of proteins was abandoned, while work on the use of metabolite biomarkers for preeclampsia risk assessment is ongoing. Future plans In accordance with the original goals of the IMPROvED study, the data and biobank are now available for international collaboration to conduct high quality research into the cause and prevention of adverse pregnancy outcomes.

Longitudinal observation of tRNA-derived fragments profiles in gestational diabetes mellitus and its diagnostic value.

Gestational Diabetes Mellitus (GDM) poses significant risks to maternal and fetal health. Current diagnostic methods based on glucose tolerance tests have limitations for early detection. tRNA-derived small RNAs (tsRNAs) have emerged as potential molecular regulators in various diseases, including metabolic disorders. However, the diagnostic value of tsRNAs in plasma for early GDM or postpartum remains unclear. This longitudinal study profiled the expression of tsRNAs across different gestational stages and postpartum in women with GDM (n = 40) and healthy control gestational women (HCs, n = 40). High-throughput small RNA sequencing identified candidate tsRNAs, which were then validated and correlated with clinical biochemical markers such as fasting blood glucose (FBG), HOMA-IR, and GHbA1c. tRF-1:32-Val-AAC-1-M6, tRF-1:31-Glu-CTC-1-M2, and tRF-1:30-Gly-CCC-1-M4 were consistently upregulated in the GDM group compared to HCs during the second trimester (p < 0.05). Only tRF-1:31-Glu-CTC-1-M2 was highly expressed during the first trimester, and tRF-1:30-Gly-CCC-1-M4 increased during postpartum. tRF-1:31-Glu-CTC-1-M2 showed a significant correlation with FBG levels in the first trimester (R = 0.317, p = 0.047). The expression of tRF-1:30-Gly-CCC-1-M4 was significantly correlated with HOMA-IR (r = 0.65, p < 0.001) and GHBA1c (r = 0.33, p = 0.037) during postpartum. A joint diagnostic model incorporating tsRNAs expression and clinical markers demonstrated enhanced predictive power for GDM (ROC AUC = 0.768). Our results revealed distinct expression patterns of specific tsRNAs in GDM, showcasing their correlation with key metabolic parameters. This underscores their promising role as biomarkers for early prediction and diagnosis of GDM. The integration of tRFs into a composite biomarker panel holds the potential to improve clinical outcomes by enabling personalized risk assessment and targeted interventions.

Largescale multicenter study of a serum metabolite biomarker panel for the diagnosis of breast cancer

Breast cancer (BC) is currently the most prevalent malignancy worldwide, and finding effective non-invasive biomarkers for routine clinical detection of BC remains a significant challenge. Here, we performed non-targeted and targeted metabolomics analysis on the screening, training and validation cohorts of serum samples from 1,947 participants. A metabolite biomarker model including glutamate, erythronate, docosahexaenoate, propionylcarnitine, and patient's age was established for detecting BC. This model demonstrated better diagnostic performance than carbohydrate antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) alone in discriminating BC from healthy controls both in the training and validation cohorts [area under the curve (AUC), 0.954; sensitivity, 87.1% and specificity, 93.5% for the training cohort and 0.834, 68.3%, and 85.2%, respectively, for the validation cohort 1]. This study has established a noninvasive approach for the detection of BC, which shows potential as a suitable supplement to the clinical screening methods currently employed for BC.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.

Unraveling the Hippocampal Molecular and Cellular Alterations behind Tramadol and Tapentadol Neurobehavioral Toxicity.

Tramadol and tapentadol are chemically related opioids prescribed for the analgesia of moderate to severe pain. Although safer than classical opioids, they are associated with neurotoxicity and behavioral dysfunction, which arise as a concern, considering their central action and growing misuse and abuse. The hippocampal formation is known to participate in memory and learning processes and has been documented to contribute to opioid dependence. Accordingly, the present study assessed molecular and cellular alterations in the hippocampal formation of Wistar rats intraperitoneally administered with 50 mg/kg tramadol or tapentadol for eight alternate days. Alterations were found in serum hydrogen peroxide, cysteine, homocysteine, and dopamine concentrations upon exposure to one or both opioids, as well as in hippocampal 8-hydroxydeoxyguanosine and gene expression levels of a panel of neurotoxicity, neuroinflammation, and neuromodulation biomarkers, assessed through quantitative real-time polymerase chain reaction (qRT-PCR). Immunohistochemical analysis of hippocampal formation sections showed increased glial fibrillary acidic protein (GFAP) and decreased cluster of differentiation 11b (CD11b) protein expression, suggesting opioid-induced astrogliosis and microgliosis. Collectively, the results emphasize the hippocampal neuromodulator effects of tramadol and tapentadol, with potential behavioral implications, underlining the need to prescribe and use both opioids cautiously.

Development of quantitative detection methods for four Alzheimer's disease specific biomarker panels using electrochemical immunosensors based on enzyme immunoassay.

Accurate and timely diagnosis of Alzheimer's disease (AD) is necessary to maximize the effectiveness of treatment and using biomarkers for diagnosis is attracting attention as a minimally invasive method with few side effects. Electrochemical immunosensor (EI) is a method that is in the spotlight in the medical and bioanalytical fields due to its portability and field usability. Here, we quantified four AD specific biomarkers using EIs based on enzyme immunoassay. We selected and developed quantitative methods for the biomarkers using screen-printed gold electrodes. For three biomarkers, quantification was performed using competition immunoassays in which antigen-antibody premix mixtures were applied to antigen-immobilized electrodes and the limit of detection (LOD) values were secured, 1.20 ng/ml, 1.30 ng/ml, and 1.74 ng/ml, respectively. For the other, a sandwich immunoassay using antibody pair was selected for quantification and LOD was also achieved as 0.077 ng/ml. All four biomarkers in buffer samples were successfully quantified and reliable R2 values were obtained, and reliable calibration curves were secured for three biomarkers in spiked human serum samples. The immunosensors developed and will be optimized are expected to be used in various fields, including detection of biomarkers for not only AD but also related diseases.